code: def testDistanceFromGreedyMetric(self):
      batch_size = 11
      num_actions = 12
      traj = self._create_batched_trajectory(batch_size)
  
      def estimated_reward_fn(unused_observation):
          return tf.stack(
              [tf.range(num_actions, dtype=tf.float32)] * batch_size
          )
  
      metric = tf_metrics.DistanceFromGreedyMetric(estimated_reward_fn)
      self.evaluate(metric.init_variables())
      traj_out = metric(traj)
      deps = tf.nest.flatten(traj_out)
      with tf.control_dependencies(deps):
          result = metric.result()
      result_ = self.evaluate(result)
      self.assertEqual(result_, 6)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.bandits.metrics import tf_metrics
  from tf_agents.bandits.policies import constraints
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 10 for calling the TFMetricsTest obj's underlying member method code with example usage: obj.testDistanceFromGreedyMetric() and returns: tf